868 resultados para Face recognition from video


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Motivated by a recently proposed biologically inspired face recognition approach, we investigated the relation between human behavior and a computational model based on Fourier-Bessel (FB) spatial patterns. We measured human recognition performance of FB filtered face images using an 8-alternative forced-choice method. Test stimuli were generated by converting the images from the spatial to the FB domain, filtering the resulting coefficients with a band-pass filter, and finally taking the inverse FB transformation of the filtered coefficients. The performance of the computational models was tested using a simulation of the psychophysical experiment. In the FB model, face images were first filtered by simulated V1- type neurons and later analyzed globally for their content of FB components. In general, there was a higher human contrast sensitivity to radially than to angularly filtered images, but both functions peaked at the 11.3-16 frequency interval. The FB-based model presented similar behavior with regard to peak position and relative sensitivity, but had a wider frequency band width and a narrower response range. The response pattern of two alternative models, based on local FB analysis and on raw luminance, strongly diverged from the human behavior patterns. These results suggest that human performance can be constrained by the type of information conveyed by polar patterns, and consequently that humans might use FB-like spatial patterns in face processing.

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Adults' expert face recognition is limited to the kinds of faces they encounter on a daily basis (typically upright human faces of the same race). Adults process own-race faces holistically (Le., as a gestalt) and are exquisitely sensitive to small differences among faces in the spacing of features, the shape of individual features and the outline or contour of the face (Maurer, Le Grand, & Mondloch, 2002), however this expertise does not seem to extend to faces from other races. The goal of the current study was to investigate the extent to which the mechanisms that underlie expert face processing of own-race faces extend to other-race faces. Participants from rural Pennsylvania that had minimal exposure to other-race faces were tested on a battery of tasks. They were tested on a memory task, two measures of holistic processing (the composite task and the part/whole task), two measures of spatial and featural processing (the JanelLing task and the scrambledlblurred faces task) and a test of contour processing (JanelLing task) for both own-and other-race faces. No study to date has tested the same participants on all of these tasks. Participants had minimal experience with other-race faces; they had no Chinese family members, friends or had ever traveled to an Asian country. Results from the memory task did not reveal an other-race effect. In the present study, participants also demonstrated holistic processing of both own- and other-race faces on both the composite task and the part/whole task. These findings contradict previous findings that Caucasian adults process own-race faces more holistically than other-race faces. However participants did demonstrate an own-race advantage for processing the spacing among features, consistent with two recent studies that used different manipulations of spacing cues (Hayward et al. 2007; Rhodes et al. 2006). They also demonstrated an other-race effect for the processing of individual features for the Jane/Ling task (a direct measure of featural processing) consistent with previous findings (Rhodes, Hayward, & Winkler, 2006), but not for the scrambled faces task (an indirect measure offeatural processing). There was no own-race advantage for contour processing. Thus, these results lead to the conclusion that individuals may show less sensitivity to the appearance of individual features and the spacing among them in other-race faces, despite processing other-race faces holistically.

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Most face recognition approaches require a prior training where a given distribution of faces is assumed to further predict the identity of test faces. Such an approach may experience difficulty in identifying faces belonging to distributions different from the one provided during the training. A face recognition technique that performs well regardless of training is, therefore, interesting to consider as a basis of more sophisticated methods. In this work, the Census Transform is applied to describe the faces. Based on a scanning window which extracts local histograms of Census Features, we present a method that directly matches face samples. With this simple technique, 97.2% of the faces in the FERET fa/fb test were correctly recognized. Despite being an easy test set, we have found no other approaches in literature regarding straight comparisons of faces with such a performance. Also, a window for further improvement is presented. Among other techniques, we demonstrate how the use of SVMs over the Census Histogram representation can increase the recognition performance.

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A depth-based face recognition algorithm specially adapted to high range resolution data acquired by the new Microsoft Kinect 2 sensor is presented. A novel descriptor called Depth Local Quantized Pattern descriptor has been designed to make use of the extended range resolution of the new sensor. This descriptor is a substantial modification of the popular Local Binary Pattern algorithm. One of the main contributions is the introduction of a quantification step, increasing its capacity to distinguish different depth patterns. The proposed descriptor has been used to train and test a Support Vector Machine classifier, which has proven to be able to accurately recognize different people faces from a wide range of poses. In addition, a new depth-based face database acquired by the new Kinect 2 sensor have been created and made public to evaluate the proposed face recognition system.

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It has been suggested that the deleterious effect of contrast reversal on visual recognition is unique to faces, not objects. Here we show from priming, supervised category learning, and generalization that there is no such thing as general invariance of recognition of non-face objects against contrast reversal and, likewise, changes in direction of illumination. However, when recognition varies with rendering conditions, invariance may be restored, and effects of continuous learning may be reduced, by providing prior object knowledge from active sensation. Our findings suggest that the degree of contrast invariance achieved reflects functional characteristics of object representations learned in a task-dependent fashion.

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This paper presents a new method for human face recognition by utilizing Gabor-based region covariance matrices as face descriptors. Both pixel locations and Gabor coefficients are employed to form the covariance matrices. Experimental results demonstrate the advantages of this proposed method.

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In the visual perception literature, the recognition of faces has often been contrasted with that of non-face objects, in terms of differences with regard to the role of parts, part relations and holistic processing. However, recent evidence from developmental studies has begun to blur this sharp distinction. We review evidence for a protracted development of object recognition that is reminiscent of the well-documented slow maturation observed for faces. The prolonged development manifests itself in a retarded processing of metric part relations as opposed to that of individual parts and offers surprising parallels to developmental accounts of face recognition, even though the interpretation of the data is less clear with regard to holistic processing. We conclude that such results might indicate functional commonalities between the mechanisms underlying the recognition of faces and non-face objects, which are modulated by different task requirements in the two stimulus domains.

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Whereas previous research has demonstrated that trait ratings of faces at encoding leads to enhanced recognition accuracy as compared to feature ratings, this set of experiments examines whether ratings given after encoding and just prior to recognition influence face recognition accuracy. In Experiment 1 subjects who made feature ratings just prior to recognition were significantly less accurate than subjects who made no ratings or trait ratings. In Experiment 2 ratings were manipulated at both encoding and retrieval. The retrieval effect was smaller and nonsignificant, but a combined probability analysis showed that it was significant when results from both experiments are considered jointly. In a third experiment exposure duration at retrieval, a potentially confounding factor in Experiments 1 and 2, had a nonsignificant effect on recognition accuracy, suggesting that it probably does not explain the results from Experiments 1 and 2. These experiments demonstrate that face recognition accuracy can be influenced by processing instructions at retrieval.

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We address the problem of 3D-assisted 2D face recognition in scenarios when the input image is subject to degradations or exhibits intra-personal variations not captured by the 3D model. The proposed solution involves a novel approach to learn a subspace spanned by perturbations caused by the missing modes of variation and image degradations, using 3D face data reconstructed from 2D images rather than 3D capture. This is accomplished by modelling the difference in the texture map of the 3D aligned input and reference images. A training set of these texture maps then defines a perturbation space which can be represented using PCA bases. Assuming that the image perturbation subspace is orthogonal to the 3D face model space, then these additive components can be recovered from an unseen input image, resulting in an improved fit of the 3D face model. The linearity of the model leads to efficient fitting. Experiments show that our method achieves very competitive face recognition performance on Multi-PIE and AR databases. We also present baseline face recognition results on a new data set exhibiting combined pose and illumination variations as well as occlusion.

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In this work we explore the multivariate empirical mode decomposition combined with a Neural Network classifier as technique for face recognition tasks. Images are simultaneously decomposed by means of EMD and then the distance between the modes of the image and the modes of the representative image of each class is calculated using three different distance measures. Then, a neural network is trained using 10- fold cross validation in order to derive a classifier. Preliminary results (over 98 % of classification rate) are satisfactory and will justify a deep investigation on how to apply mEMD for face recognition.

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In this paper we address the problem of face detection and recognition of grey scale frontal view images. We propose a face recognition system based on probabilistic neural networks (PNN) architecture. The system is implemented using voronoi/ delaunay tessellations and template matching. Images are segmented successfully into homogeneous regions by virtue of voronoi diagram properties. Face verification is achieved using matching scores computed by correlating edge gradients of reference images. The advantage of classification using PNN models is its short training time. The correlation based template matching guarantees good classification results